Six pitfalls to avoid when building a data-driven organization

More and more organizations are beginning to introduce a data driven approach into their ways of working. Despite their best intentions, a significant proportion will ultimately fail. Many are victims of the same pitfalls. In this post, Marcus Ehrndal, Head of the Data & Analytics team at BearingPoint Sweden, identifies some of the most common mistakes organizations make when attempting to adopt data and analytics into their ways of working and suggest ways you can avoid following in their footsteps.

There are numerous benefits to working in a data driven way. Here are our top three:

Data driven decision-making is much more trustworthy than relying on your gut instinct

Using a holistic dataset rather than a smaller statistical sample to make decisions is more accurate and valuable

Automating elements of the decision-making process can drastically improve efficiency and be kinder on your bottom line

At BearingPoint, we have an in-depth understanding of how to design, plan, and execute data and analytics initiatives. For more than 15 years we have partnered with some of the largest players in finance, insurance, and retail, as well as huge public infrastructure entities. These experiences have shaped our outlook on how to best utilize data and analytics and to identify when this resource is being used incorrectly.

Below is a list of some of the most common pitfalls to avoid when building a data-driven organization as well as some potential solutions. If you are doing any of the following, stop now, and get in touch with us!

Lack of strategic alignment – remember to focus on what value you need to create instead of what data you have available. At the end of the day, scaling requires buy-in from the rest of the organization. In order to make that happen, you need to ensure that you are using data to reach strategically important goals. These are the targets we recommend to prioritize.

Not defining how you intend to use the results and what value it will create – it is not enough to have a broad idea of the goal you aim to achieve. We recommend you to think beyond the initial start-up phase of the project and describe, in detail, how you will implement and operationalize the analytics model or dashboard you propose creating so that it actually produces discernible benefits. This sounds obvious, but it is a crucial step that is often overlooked until after a project has got well underway — by then it is often too late.

Technology overload – not picking the right tool for the job. While tempting, do not use advanced tools simply for the sake of it. In most cases, this will eventually limit or reduce the efficiency of the decision-making process that you are trying to facilitate. An elegant and simple solution is usually a lot simpler, cheaper, and easier for the average organization to use. Don’t overcomplicate things.

Waiting for the perfect infrastructure – don’t waste time waiting for the ideal opportunity to get started. Infrastructure and data quality, for example, can always be improved. But oftentimes, good, is good enough. Instead of waiting for the perfect moment, get your analytics project underway today.

Underwhelming UX – presentation matters. Your data and findings might be excellent but is the presentation of your results equally top-notch? If your dashboard or report looks terrible it can have a negative impact on what you are trying to communicate. Do not underestimate this point if you truly want the support, understanding and buy-in from the whole organization.

No traction – struggling with implementation. Your proof of concept might look like it’s going to be a success and bring value to the organization but once you try to make it an established process, you hit hurdle after hurdle. In the end, nothing tangible really happens. If you targeted a strategically important goal – as summarized in point 1 above – it will make it easier to implement the new processes because you should have backing from the organization. Fundamentally, no sustained value is gained until the data driven methodology is engrained in the process, so make sure to finish what you started instead of jumping from pilot to pilot.

As you can see, collecting and analyzing data without a clear goal in mind is risky behavior for every organization – and yet it happens. Perhaps in an attempt to get in on a good thing, many organizations get ahead of themselves before defining expectations, goals, uses etc.

The next time you initiate a data and analytics project, keep the above points in mind. Doing so will ensure that data collection and data analysis will be much more efficient, and the results will be more valuable.

If you are mid-project and some of the points sound a little too familiar, we encourage you to reach out before it’s too late. We are happy to discuss your current situation and suggest steps to get you and your organization back on track.